Overview
Last updated: 2019-05-30
Checks: 6 0
Knit directory: ESG.Mapping/
This reproducible R Markdown analysis was created with workflowr (version 1.3.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20190528) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.
Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
Ignored files:
Ignored: .Rhistory
Ignored: .Rproj.user/
Ignored: data/raw_data/
Ignored: manuscript/figures/
Untracked files:
Untracked: data/derived_data/vector/
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | df8641b | jjmaynard | 2019-05-29 | Build site. |
| Rmd | a72c7d7 | jjmaynard | 2019-05-29 | Index update |
| html | a72c7d7 | jjmaynard | 2019-05-29 | Index update |
| html | a16932a | jjmaynard | 2019-05-29 | Build site. |
| html | 262b6e4 | jjmaynard | 2019-05-29 | Build site. |
| Rmd | 3e21aed | jjmaynard | 2019-05-29 | Updated Index file |
| html | 3e21aed | jjmaynard | 2019-05-29 | Updated Index file |
| html | 4912c29 | jjmaynard | 2019-05-29 | Build site. |
| html | 997b636 | jjmaynard | 2019-05-29 | Build site. |
| html | 166c6ce | jjmaynard | 2019-05-29 | Build site. |
| Rmd | ad27dd0 | jjmaynard | 2019-05-29 | Update index |
| html | 9fc2c9c | jjmaynard | 2019-05-29 | Build site. |
| html | aafe6dd | jjmaynard | 2019-05-29 | Build site. |
| Rmd | 312851b | jjmaynard | 2019-05-29 | rrtools workflowr merge |
| html | 312851b | jjmaynard | 2019-05-29 | rrtools workflowr merge |
Sandy ESG near Las Cruces, NM (Summerford Mountain in background) – March 22, 2019. Photo credit: Shawn Salley
Ecological site descriptions (ESDs) and associated state-and-transition models (STMs) provide a nationally consistent classification and information system for defining ecological land units for management applications in the US. Current spatial representations of ESDs, however, occur via soil mapping and are therefore confined to the spatial resolution used to map soils within a survey area. Land management decisions occur across a range of spatial scales and therefore require ecological information that spans similar scales. Digital mapping provides an approach for optimizing the spatial scale of modeling products to best serve decision makers and have the greatest impact in addressing land management concerns.
This website describes the data and code used in the paper:
Maynard, J.J., T.W. Nauman, S.W. Salley, B.T. Bestelmeyer, M.C. Duniway, C.J. Talbot, and J.R. Brown, (2019). Digital Mapping of Ecological Land Units using a Nationally Scalable Modeling Framework. Soil Science Society of America Journal https://doi.org/doi:10.2136/sssaj2018.10.0346
How to cite
Please cite this compendium as:
Maynard, J.J., T.W. Nauman, S.W. Salley, B.T. Bestelmeyer, M.C. Duniway, C.J. Talbot, and J.R. Brown, (2019). Compendium of R code and data for Digital Mapping of Ecological Land Units using a Nationally Scalable Modeling Framework. Accessed 30 May 2019. Online at https://doi.org/doi:10.2136/sssaj2018.10.0346
This research compendium describes the spatial modeling framework, R code, and data used to create digital maps of ecological sites in two study areas within the western U.S., representing 6.1 million ha on the Colorado Plateau and 7.5 million ha within the Chihuahuan Desert.
Our modeling framework employs machine learning algorithms, soil survey field observations, soil survey geographic databases, ecological site data, and a suite of remote sensing-based spatial covariates (e.g., hyper-temporal remote sensing, terrain attributes, climate data, land-cover, lithology) to generate probability-based spatail predictions of ecological site groups.
Modeling framework for the spatial prediction of ecological land units.
The ecological site modeling framework shown above consists of five steps:
1. Develop an ESG point dataset for training and cross-validation
2. Preprocess ESG covariates for tiling and point overlay
3. Covariate feature selection and model development
4. Spatial predictions using tiled raster stacks
5. Model validation and uncertainty analysis All modeling steps outlined above were implemented using Open-Source software, including: SAGA GIS, GDAL, and R.
sessionInfo()
#> R version 3.5.2 (2018-12-20)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows 7 x64 (build 7601) Service Pack 1
#>
#> Matrix products: default
#>
#> locale:
#> [1] LC_COLLATE=English_United States.1252
#> [2] LC_CTYPE=English_United States.1252
#> [3] LC_MONETARY=English_United States.1252
#> [4] LC_NUMERIC=C
#> [5] LC_TIME=English_United States.1252
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] here_0.1 sf_0.7-3 dplyr_0.8.0.1 leafsync_0.1.0
#> [5] leaflet_2.0.2 mapview_2.7.0
#>
#> loaded via a namespace (and not attached):
#> [1] tidyselect_0.2.5 xfun_0.6 purrr_0.3.2
#> [4] lattice_0.20-38 colorspace_1.4-1 htmltools_0.3.6
#> [7] stats4_3.5.2 viridisLite_0.3.0 yaml_2.2.0
#> [10] base64enc_0.1-3 rlang_0.3.4 leafpop_0.0.1
#> [13] e1071_1.7-1 later_0.8.0 pillar_1.3.1
#> [16] glue_1.3.1 DBI_1.0.0 RColorBrewer_1.1-2
#> [19] sp_1.3-1 stringr_1.4.0 munsell_0.5.0
#> [22] workflowr_1.3.0 raster_2.8-19 htmlwidgets_1.3
#> [25] codetools_0.2-15 evaluate_0.13 knitr_1.23
#> [28] httpuv_1.5.1 crosstalk_1.0.0 class_7.3-14
#> [31] leafem_0.0.1 Rcpp_1.0.1 xtable_1.8-3
#> [34] promises_1.0.1 scales_1.0.0 backports_1.1.4
#> [37] classInt_0.3-1 satellite_1.0.1 jsonlite_1.6
#> [40] webshot_0.5.1 mime_0.6 fs_1.2.7
#> [43] png_0.1-7 digest_0.6.19 stringi_1.4.3
#> [46] shiny_1.3.1 grid_3.5.2 rprojroot_1.3-2
#> [49] tools_3.5.2 magrittr_1.5 tibble_2.1.1
#> [52] crayon_1.3.4 whisker_0.3-2 pkgconfig_2.0.2
#> [55] assertthat_0.2.1 rmarkdown_1.12 R6_2.4.0
#> [58] units_0.6-2 git2r_0.25.2.9000 compiler_3.5.2